Systems and methods for predicting and pricing of gross rating point scores by modeling viewer data

ABSTRACT

Systems and methods are disclosed for characterizing websites and viewers, for predicting GRPs (Gross Rating Points) for online advertising media campaigns, and for pricing media campaigns according to GRPs delivered as opposed to impressions delivered. To predict GRPs for a campaign, systems and methods are disclosed for first characterizing polarized websites and then characterizing polarized viewers. To accomplish this, a truth set of viewers with known characteristics is first established and then compared with historic and current media viewing activity to determine a degree of polarity for different Media Properties (MPs)—typically websites offering ads—with respect to gender and age bias. A broader base of polarized viewers is then characterized for age and gender bias, and their propensity to visit a polarized MP is rated. Based on observed and calculated parameters, a GRP total is then predicted and priced to a client/advertiser for an online ad campaign.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 61/779,231 filed Mar. 13, 2013.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to systems and methods forrating the success of online advertising campaigns as well as predictingthe success of online advertising campaigns and pricing campaigns basedon the predictions. It also relates to systems and methods forcharacterizing viewer behavior and determining to what degree a specificviewer's behavior places them in a specific category with respect totheir behavior, thus becoming a polarized viewer. The invention alsorelates to systems and methods for characterizing websites with respectto polarized characteristics.

2. Prior Art

According to www.wikipedia.org®, Gross Rating Point (GRP) is a term usedin advertising to measure the size of an audience reached by a specificmedia vehicle or schedule. It is the product of the percentage of thetarget audience reached by an advertisement, times the frequency theysee it in a given campaign (frequency×% reached). For example, atelevision advertisement that is aired 5 times reaching 50% of thetarget audience each time it is aired would have a GRP of 250 (5×50%).To achieve a common denominator and compare media, reach×frequency areexpressed over time (divided by time) to determine the ‘weight’ of amedia campaign. GRPs are used predominantly as a measure of media withhigh potential exposures or impressions, like outdoor, broadcast, oronline (Internet).

GRP values are commonly used by media buyers to compare the advertisingstrength of various media vehicles, including in recent years, onlineadvertising on the Internet. All GRP calculations to date arehistorical, being compiled after a campaign completes. Video adstypically contain a pixel pattern called a “tracking pixel” supportedby, for instance, Nielsen®. For example, if a user logs onto Facebook®(a Nielsen media partner) and then visits another website where an adthat Nielsen is tracking is shown, Nielsen will put a pixel in the adthat will prompt Facebook to send Nielsen the age and gender of thepeople who viewed the ad. Nielsen can then match the IP address of thepixel to see if the person is also on a Nielsen panel. If so, theinformation from the third-party partner can be combined with the paneldemographics. This mechanism enables Nielsen to report on the GRPsdelivered on a specific online ad campaign after the campaign hascompleted.

In the RTB (Real-Time Bidding) environment for electronic mediaimpression auctions, an electronic advertising agency/consolidatoroperating a demand-side platform receives billions of daily auctionopportunities for electronic media impressions from partners likeGoogle®, Yahoo®, etc. These partners operate auctions for ad impressionsand then place electronic ads based on auction results. A partner'sauction is considered an external auction with respect to a demand-sideplatform where an internal auction may also be operated to determinewhich advertisements, also referred to herein as ads, and bids aresubmitted to the external auction. Each ad impression opportunityincludes information parameters about the ad impression—for example, thetarget website, geolocation of the user, ad size, user cookie, etc, thatare used for targeting purposes. The demand side platform then processeshundreds of ads in their system, supplied by advertiser clients alongwith desired filtering/targeting parameters, against informationparameters supplied by the partner, and filters out any ads that do notqualify (for example the ad does not want to target youtube.com®). Forads that are not removed due to a mismatch with targeting parameters,the demand-side platform then evaluates the corresponding bids thatrepresent how much each client advertiser is willing to pay. The winningbid in the internal auction is then sent to the external auction tocompete for the impression opportunity.

An electronic advertising agency/consolidator operating a demand-sideplatform typically charges their advertiser/clients based on impressionsafter the fact. They have not previously been known to guarantee thereach of a campaign ahead of time—and do so at a guaranteed price.

Note that in some scenarios, the electronic advertisingagency/consolidator operating a demand-side platform and theadvertiser/client may in fact be the same entity—for instance when theycomprise a large organization with an internal advertising departmentcapable of acting as a demand-side platform. Also, in such an instance,there may be no internal auction—just a submission to an externalauction.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter that is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other objects, features, andadvantages of the invention will be apparent from the following detaileddescription taken in conjunction with the accompanying drawings.

FIG. 1 shows an overview block diagram showing system components anddata flow for a demand side platform according to the invention.

FIG. 2 shows an overview block diagram showing system components anddata flow for website and viewer polarization profiling as well as GRPprediction and quoting according to the invention.

FIG. 3 shows a flowchart with exemplary and non-limiting methodsdescribed for the determination of site polarization as well asdetermination of polarization for unknown viewers in order to classifythem as known polarized viewers.

FIG. 4 shows a flowchart for the process whereby prices quoted to anadvertiser client for on target GRPs delivered for an electronicadvertising campaign based on site polarization profiles.

FIG. 5 shows an exemplary process for choosing and aggregating MediaProperties (MPs) automatically to determine available campaigninventory.

FIG. 6 shows an exemplary process for choosing and aggregating MPs todetermine available campaign inventory wherein a client specificallychooses MPs to be included in the campaign.

FIG. 7 shows an exemplary process for operating an electronicadvertising campaign whereby a quantity of on-target GRPs are providedto a client advertiser at a predetermined quoted price, and from time totime after a campaign has been completed, an Efficiency Factor isadjusted based on GRP data acquired from a third party such as forexample, Nielsen.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Systems and methods are disclosed for characterizing websites andviewers, for predicting GRPs (Gross Rating Points) for onlineadvertising media campaigns, and for pricing media campaigns accordingto GRPs delivered as opposed to impressions delivered. To predict GRPsfor a campaign, systems and methods are disclosed for firstcharacterizing polarized websites and then characterizing polarizedviewers. To accomplish this, a truth set of viewers with knowncharacteristics is first established and then compared with historic andcurrent media viewing activity to determine a degree of polarity fordifferent Media Properties (MPs)—typically websites offering ads—withrespect to gender and age bias. A broader base of polarized viewers isthen characterized for age and gender bias, and their propensity tovisit a polarized MP is rated. Based on observed and calculatedparameters, a GRP total is then predicted and priced to aclient/advertiser for an online ad campaign.

Even if a polarization profile for a specific viewer is not known, it isuseful to understand the polarization profile or probability for awebsite where that viewer is about to be offered an ad impression, and aGRP expectation can be computed for such scenarios as described herein.Further, creating a database of polarized websites that have each beenprofiled according to their polarization probabilities with respect tocertain Viewer Characteristics (VCs) is useful not only in estimatingGRPs for a campaign. It is also useful as a component of an exemplaryprocess as described herein for profiling unknown viewers in order toclassify them and create a database of polarized viewers.

Profiling Polarization of Media Properties

A key function of the processes described herein is to determine the“polarization” of a Media Property. A Media Property or MP represents aspecific instance of a media platform for electronically deliveringinformation to a viewer. An MP as referenced herein usually refers to awebsite or URL on the Internet, however may also refer for example andwithout limitation to an App ID, a Game ID, or other electronic mediaincluding for example electronic billboards. Polarization in generalrefers to the extent that a particular MP, or as will later bedescribed, a particular viewer, has characteristics that are biased (ornot biased) with respect to certain targeting criteria. Polarizationratings are usually expressed in terms of probabilitypercentages—however other rating methods may be used. Targetingcharacteristics most commonly utilized for polarity rating are typicallyage and gender, although other characteristics may be also rated withoutlimitation. Viewer age is typically broken down into age brackets, forexample 12-17, 18-34, 35-44, etc. Viewers are commonly identified bytheir electronic “cookie” passed from their computer to a site they arevisiting, and as such a process for classification of viewers accordingto various viewer characteristics is sometimes known as “cookiebucketing”. Note that a particular viewer may in fact use multiplecomputers and therefore have multiple cookies. While multiple cookiesmay typically be treated as multiple viewers, it is possible to treatthem as the same viewer if sufficient information on a viewer and theircomputer use is known. For the sake of non-limiting examples presentedherein, each cookie is assumed to represent a different viewer and theterms “viewer” and “cookie” are assumed to be synonymous.

FIG. 1 shows an overview block diagram describing system components anddata flow for a demand side platform according to the invention with afocus on information conveyed relative to polarization profiling of MPsand viewers, and for estimating and quoting GRPs (Gross Rating Points)to an advertiser client 116 provided by a demand-side platform 114. GRPestimation according to the invention includes first establishingdatabases of known polarized MPs as well as a database of knownpolarized viewers as described with respect to FIGS. 2 and 3. Per FIG.1, an ad slot opportunity 104 on a webpage 106 offered by an exemplarymedia property 102 is offered in an auction for an impressionopportunity. Here, an advertisement is to be placed in ad slot 104 onwebpage 106 to be viewed by a specific viewer 108. Media property 102sends a bid request package 110 consisting of viewer identificationinformation for viewer 108 and criteria specific to ad slot 104. Thisbid request package is received on one or more servers 112 where thedemand-side platform 114 operates, and this information is processedthereon. Subsequently, if the impression opportunity fits the targetingcriteria of one or more advertiser clients 116, the demand-side platformwill respond with a bid response 118 which includes the advertisementitself as well as a bid price.

This particular impression opportunity may fit with a previously definedadvertising campaign for one or more advertiser clients 116. For suchcampaigns, the demand-side platform 114 may have previously provided aprice quote 122 for such campaign. As opposed to simply quotingimpressions to be purchased, according to the invention such a campaignmay be quoted in terms of GRPs delivered, essentially guaranteeingviewing reach for specific targeting criteria. In order to receive sucha campaign price quote 122, an advertiser client 116 would havepreviously delivered to the demand-side platform a request for aquotation including information package 120. Information package 120includes for example and without limitation: GRPs desired; campaigntargeting parameters; and campaign runtime.

FIG. 2 shows an overview block diagram describing system components anddata flow for website and viewer polarization profiling as well as GRPprediction and quoting according to the invention. Here, a polarizationprofiling engine 202 operating on one or more processors/servers 112operates according to the exemplary flow described in FIG. 3, firstcreating a database of polarized MPs 206 based on the activity ofviewers in truth set 204. Viewers in truth set 204 are characterized atleast by gender, however may also be characterized for example andwithout limitation by age, geographic locations (“geos”), and othercharacteristics. Subsequently a database of known polarized viewers 208is created by the polarization profiling engine. Optionally, look-alikeviewers may be categorized as described herein and added to the databaseof known viewers based on comparing with known polarized viewers.

Subsequently, an advertiser client 210 may supply an information package120 to the demand-side platform including a desired campaign runtime212, a quantity of GRPs desired 214 for a campaign, and targetingcharacteristics 216 for the campaign. In response, GRP prediction andquoting engine 220 operating on one or more processors/servers 112provides a GRP price quote 222 to advertiser client 210. Should theadvertiser client find the quote acceptable they will normally proceedto engage with the demand-side platform to execute the campaign. Whenthe campaign is completed, a package of historical campaign data 224 isobtained from Nielsen® in order to validate the reach of the campaign.

As shown in flowchart 300 of FIG. 3, a first phase of an exemplary andnon-limiting polarization characterization process according to theinvention involves a determination 330 of polarization characteristicsfor different MPs (typically websites) that viewers may visit. Withinthis process, a first step is to establish 302 a “Truth Set” ofviewers/cookies by purchasing or otherwise acquiring data. A truth setis a database of specific viewers including their cookies and knowncharacteristics for those viewers such as for example age and gender.Once a truth set is available, it is then characterized for one or moreViewer Characteristics (VCs) per step S302.

Subsequently per step S304, records of past Internet visits are searchedand analyzed relative to the behavior of different viewers going back intime by a specified number of months. Where a viewer in the records ofpast Internet visits belongs to the truth set, counters are incrementedfor each VC (gender, age group, etc) for each MP (MediaProperty—Site/domain, App ID, Game ID, etc.) visited by the viewer. Oncethis process is finished, at least an empirical male/female frequency orprobability has been established for every Media Property matched by atleast one viewer/cookie from the truth set. In a similar way, each MP isalso profiled for polarization with respect to viewers/visitors indifferent age brackets and any other VC category of interest.

With respect to for instance gender, statistically the genderdistribution is expected to be approximately 50:50 in the generalInternet populace, and therefore it is appropriate to then normalize 306distributions for each media property to account for any biases in theTruth Set distribution. In order to accomplish this, the gains to beapplied to the Male and Female probabilities are computed as follows:

First, the number of viewers/cookies representing the “Least FrequentGender” is calculated to be equal to the minimum number of either the(Females in the Truth Set) or the (Males in the Truth Set). Then thegain factor for each gender subset is calculated as follows:Gain for Females=Least Frequent Gender/Females in Truth SetGain for Males=Least Frequent Gender/Males in Truth Set

Then, the Unbiased Probability (“P”) for each gender at each mediaproperty (MP) is determined S308 as follows:P(Female) for MP=Gain for Females*(Female Count for MP/Total Cookies atMP)P(Male) for MP=Gain for Males*(Male Count for MP/Total Cookies at MP)

At this point, a database of polarized MPs has been created where foreach MP, a polarization probability exists for each VC for which acharacterization determination was performed with respect to the truthset. One embodiment of GRP prediction and quoting utilizes thispolarized MP database to calculate predicted GRP reach for a proposedcampaign and to create a price quote for that campaign.

After an initial classification process for polarized websites using thetruth set per FIG. 3, MPs may be further “bucketed” or classified eachtime a viewer in the truth set visits a website, therefore furtherenhancing the classification accuracy for any MP so visited.

Polarization Profiling of Viewers

In predicting the results of a campaign it can be especially useful ifthe polarization of a potential viewer is understood when impressionopportunities arise on a particular MP for that viewer. As such, it isuseful to profile and classify unknown viewers with respect to VCs andbuild a database of known polarized viewers including a probability ofpolarization with respect to different VCs for each polarized viewer.

Choosing a set of MPs (media properties) that will allow the profilingof viewers/cookies that are not members of the truth set is done asfollows:

Per step 308, all MPs are identified whose unbiased distributions arehighly polarized towards Male or Female (or towards any other VCs beinganalyzed), and these are rated as “polarized”. Stereotypical examples ofwebsites (MPs) exhibiting extreme degrees of polarization include forinstance: Sports-oriented for Males; and Fashion-oriented for Females.

To accomplish this, a threshold is applied to the dominant gender, thatis, if the value of:Max(P(Female),P(Male))is greater than a predefined threshold, for example and withoutlimitation 0.80, then the MP is added to the Polarized Set with respectto the VC being analyzed—for instance in this example, gender. Thistypically results in 100 s to 1000 s of media properties being added toa database of polarized MPs, with varying levels of traffic beingcategorized as “polarized” or not. In all cases, the polarizationprobability for an MP with respect to each VC is recorded, and this isuseful in some embodiments of GRP estimation and quoting when not allsites chosen by an advertiser/client are highly polarized, and somesites with only moderate polarization must be included in order tofulfill the reach and/or time frame requirements of a campaign.

To categorize 340 any unknown viewer/cookie for VC polarizationprobability, for example gender (Male or Female), an exemplary processaccording to the invention keeps a running probability for each of them.By default the distribution is set at:P(Female)=0.5|P(Male)=0.5

Each time that a cookie/viewer is seen viewing a polarized MP, theprobabilities for that cookie/viewer are updated S310 as follows (withthe assumption that each auction is statistically independent):P(Male)′=P(Male)*Polarized Site P(Male)P(Female)′=P(Female)*Polarized Site P(Female)where the:Denominator for Normalization=P(Male)′+P(Female)′Therefore:P(Male)′=P(Male)′/Denominator for NormalizationP(Female)′=P(Female)′/Denominator for Normalizationwhich guarantees that the definition of probability holds, that is:P(Male)′+P(Female)′=1

Each time that a cookie/viewer is seen visiting a polarized site, theprobabilities are re-adjusted. Multiple hits on highly polarized sitesof the same orientation rapidly result in gender assessments withprobability generally exceeding 0.95.

Finally, any time it becomes useful to delineate a male or femalesegment from the database of classified polarized viewers/cookies, allmembers are analyzed and their probabilities for a particular VC arecompared S312 with a threshold for whichever direction is dominant forthe particular VC, for example in the case of gender,Max(P(Female),P(Male)).

The chosen threshold value corresponds directly to the predicted overallaccuracy for the segment, while the expected accuracy for gender (Maleand Female) for example, is equal to the mean probability across allchosen viewers/cookies. One exemplary and non-limiting threshold wouldbe 0.92, but it can be lowered to increase the size of the pool (reach)traded off against accuracy.

Per step S314, for a cookie/viewer and a particular VC, if thepolarization probability is greater S314 than the threshold value, thatCookie/Viewer is recorded S316 as polarized for the specific VC (gender,age group, etc) with the specific probability value also beingoptionally recorded in the known viewer database. If on the other hand,that cookie/viewer has a polarization probability less than thethreshold value, then the probability value for that Cookie/Viewer maybe still optionally recorded S318 for the specific VC (gender, agegroup, etc) in the known viewer database. After either steps 316 orS318, the next cookie/viewer S320 is analyzed per step 310.

Note that it is preferable that multiple cookie/site hits are notrecorded, so hitting the same site again and again won't change aviewer's probabilities. Also note that it is significant that onlyhighly polarized MPs are considered as “polarized”—using allprobabilities would result in a per-cookie assessment in which thebiases would be drowned out by the more frequently seen sites that arenot polarized.

Once a set of viewers/cookies has been thus classified with highaccuracy, they can be used as a further means to profile MPs forpolarity in a manner similar to how the truth set is utilized per theprocess of FIG. 4. In this case however, since unlike the truth set eachVC for a particular polarized viewer is typically less than 100%probable, the analysis must take into account the probability for eachVC for a particular viewer/cookie being used.

Also, the approach can extend beyond just sites/apps/games to partialURLs, verticals and any other attributes that are available in auctionprotocols. Furthermore, with the appropriate truth set, classificationcan be extended to age brackets, marital status, children in household,etc.

Extensions of the methods described herein include, for example, wherethe number of classified viewers/cookies in the known viewer database isincreased by adding “look-alikes”. Here, cookies/viewers that did nothit any polarized sites are classified based on similar behavior toclassified cookies where the classified cookies have a probabilityestablished for different VCs.

Determining Look-Alike Viewers Based on Polarized Viewers

Look-Alike modeling has been used for some time in advertising campaignsand is currently used in electronic and online advertising. In general,look-alike modeling includes selection of a trait or target segment anddata sources for analysis, including a baseline population database forcomparison. The analysis looks for viewers in the data sources that areidentical or similar to viewers in the baseline population with respectto the selected trait or target segment. Then, newly discovered traitsare ranked in order of influence or desirability. The ranking may be anumber between, for instance, 0 to 1. Ranks closer to 1 means they aremore like the audience in the baseline population. Also, heavilyweighted traits are valuable because they represent new, unique viewerswho may behave similarly to the established audience represented in thebaseline population. The result is a database of “Look-Alike Viewers”who have characteristics similar to those in a well-characterizedbaseline population. For the invention, the baseline population istypically the database of known, polarized viewers. Adding look-alikeviewers to the database of known viewers enables larger campaigns to beaddressed where the database of known polarized viewers alone is notlarge enough to meet the campaign requirements in terms of reach and/orrun time. Also, since a look-alike viewer has not been profiled by themethod described for FIG. 3, the polarization probability for alook-alike viewer may optionally be down-graded relative to that of theknown polarized viewers that were used to determine the look-alikeviewer. For example if the male gender assessment for known malepolarized viewers is 95%, then the gender assessment for a look-alikepolarized viewers might for example be 80%.

Predicting, Pricing, and Selling GRPs

While ad campaigns historically are priced by impressions, an impressiondoes not guarantee that a targeted viewer has interacted with the MP.The ability to purchase an ad campaign and know that the reach(on-target viewed ads) is guaranteed would be advantageous for anadvertiser. For a demand-side-platform or online ad agency to priceaccording to GRPs or “reach”, requires a statistical confidence in theability to supply a given degree of on-target reach for a given numberof impressions purchased, in order to offer such a service at a profit.An on-target view is one where the viewer's characteristics match thetargeted characteristics of a campaign. For instance, if an ad campaignis for men's sporting goods, male viewers are typically targeted. Whenan impression is presented to a female, such a viewing would NOT beon-target for that campaign.

A large historical database is required to support an offering capableof guaranteeing a level of GRP reach, so that the cost of reachingspecific categories of viewer can be predicted with an acceptable levelof statistical probability. According to one exemplary and non-limitingembodiment of the invention, viewer activity is bucketed over anextensive period of time where MPs (Sites) are profiled according tocharacteristics (age bracket, gender, etc.) of visiting viewers, and adegree of “polarization” is established for each MP with respect to eachviewer characteristic (VC).

Viewers are classified according to their propensity or polarization tovisit polarized websites, with respect to each VC type. Systems andmethods for creating such databases are described herein with respect toFIGS. 1-3. The cost of each viewer interaction is also accumulated.Thus, a historical summary may be referenced that indicates how many MPsare historically in a bucket (classification category), and the cost ofreaching those viewers. For any VC with respect to either an MP or aknown viewer, a polarization probability—typically described as afraction or percentage—is available as a result of the processesdescribed with respect to FIGS. 1-3.

The definition of GRPs (Gross Rating Points) for online advertising suchas that addressed herein, is (Reach×Frequency), defined morespecifically as:(number of unique views/online population segment or specific targetaudience)×(average exposures per viewer over the course of the campaign)

The process of quoting a GRP campaign to a client/advertiser begins withstep S402 of flowchart 400 of FIG. 4, where a demand-side platform oronline advertising agency receives targeting criteria from theadvertiser client. This targeting criteria includes desired VC targetingas well as any MPs and specific geographic locations (geos) the clientdesires to target. A maximum runtime for the campaign is also included.The desired campaign size (reach) in GRPs may also be provided by theadvertiser client. Subsequently according to exemplary and non-limitingembodiments described herein the demand-side platform provides aquotation (price) for GRPs that are “on target”—in other words the reachfor the campaign is guaranteed to include viewers that possess thespecific targeting characteristics specified by the client/advertiser.

In step S404, the demand-side platform determines the availableimpressions for each targeted MP and Geo as well as the polarizationprobability of each targeted MP with respect to each targeted VC, andthe historical cost of buying impressions on each of the targeted MPs.Specific MPs to be targeted for the campaign are chosen 406 according toflowcharts 500 and 600 of FIGS. 5 and 6 respectively. Per FIGS. 5 and 6,MPs are chosen and aggregated either based on client selection and/orautomatically based on the historical cost in proportion to thepolarization probability for an MP (typically described by $ per %point). At the start, the client may supply a list of MPs and geos fromwhich the demand side platform may choose automatically in order toassemble a possible campaign (per FIG. 5), or alternately, the clientmay specifically choose each MP and Geo that are to be part of thecampaign (per FIG. 6).

Per S406 the total number of impressions that must be purchased toachieve the desired level of on-target GRPs is then determined.Subsequently in S406 it is determined if the desired GRPs can beachieved in the specified campaign run time. If that is the case, theprocess proceeds to step S408. If desired on-target GRPs cannot beachieved, this result is reported to the client/advertiser.

To create a price quotation, per S408 each targeted MP and Geo areexamined to determine the cost of supplying the predicted GRPs as afunction of impressions required to achieve the targeted inventory,based on the polarization probability and the historical cost of buyingimpressions on the targeted MP/geo. In general, “inventory” is thequantity of impressions typically available on a specific MP during aspecified campaign run time. This is determined historically and themost relevant data is typically the most recent.

In general, since the polarization probability of any MP is less than100% for any VC, a larger number of impressions will need to bepurchased in order to achieve the desired number of on-target viewsrequired to provide the requested GRPs. For example, a campaign may findthat 10,000 impressions are available on YouTube during the campaign runtime. YouTube has a polarization probability value of 0.45 for Males(45% of the audience is male), so if 10,000 impressions are purchased onYouTube for a male-targeted campaign, the on-target impressions are4,500 and the potential wastage is 5,500. The cost of providing the4,500 on-target impressions is the calculated as the cost of purchasing10,000 impressions. The number of impressions to be purchased for an MPis therefore equal to the desired on-target impressions divided by thepolarization probability for that MP with respect to a targeted VC, thepolarization probability being expressed as a fraction representing aprobability percentage. Also, when a campaign is targeting more than oneVC—for instance males plus a specific age bracket such as 18-25—thepolarization probabilities for both VC should be taken into account forthat VC. One exemplary and non-limiting method to combine the effect ofboth VCs is to multiply the polarization probabilities to produce acomposite polarization probability for the MP with respect to thatspecific campaign.

Per S410, the total cost of the campaign is then determined by summingthe cost of all impressions to be purchased for all targeted MPs andgeos, and the estimated total number of delivered on-target GRPs isdetermined by summing the predicted GRP quantities for all targeted MPsand geos.

Finally per S412, a Campaign Price to be quoted is computed according tothe following exemplary and non-limiting formula:Quoted Campaign Price=(Total cost of impressions over all targeted MPsand geos)×(Efficiency Factor)×(Profit Margin Factor)

Here, the efficiency factor and profit margin factor are variable andmay be altered by the demand-side platform from one campaign to anotherdepending upon campaign results and other factors. A method foradjusting the efficiency factor from time to time is described by theprocess shown in FIG. 7.

FIGS. 5 and 6 show flowcharts for processes whereby MPs are chosen andaggregated to determine a total quantity of on-target inventory that isavailable for a specific campaign.

FIG. 5 shows a process whereby a demand-side platform may automaticallychoose which MPs are used in a campaign, either from a list of possibleMPs supplied by a client/advertiser, or alternately from all possibleMPs that have previously been profiled for polarization. According to analternate embodiment, the process may mix MPs specified by theadvertiser/client with other MPs having known polarization profiles inorder to assemble a campaign. Typically, a client/advertiser will choosewhich of these alternatives they would like the demand-side platform toutilize in assembling a campaign.

According to flow chart 500 of FIG. 5, a system according to theinvention automatically determines 502 MPs that fit a client's targetingcriteria, and also determines the “cost per point” or cost perpolarization probability percentage for each MP. Thus when selecting MPsto add to the campaign, the system can qualify the cost-effectiveness ofeach such addition. If the polarization percentage or probability wasthe sole factor evaluated in determining which MPs to add to thecampaign, a highly polarized site might be added but have such anextreme cost that it consumes the campaign budget well before thedesired number of on-target impressions has been reached. As a result itis preferable to prioritize the addition of MPs to a campaign accordingto the cost-per-point rather than simply on polarization probability.

For any MP, there is a relationship in aggregate between winnableimpressions and CPM bid, and this data is accumulated over time and isavailable to help determine what inventory is available a bid price.This is used to estimate how many impressions are buyable at any CPMbid. This data may be utilized for determining available impressions andcost. Thus, if an MP does not have enough targeted inventory at a givenbid price point, the bid price can be raised to produce more inventory,however at a higher cost per point. Thus, the bid price may be adjustedby the client/advertiser or automatically by the system in order toprovide more inventory from highly desirable polarized sites.

Per step S504, the process starts by identifying the potential target MPhaving the lowest cost per point, and determining for that MP theavailable inventory as well as that portion of the available inventorythat matches the campaign targeting criteria. This portion then becomesthe “on target” inventory or “targeted inventory” and is determinedbased on the MPs polarization probability. For example during theruntime allocated for a campaign, assume that an MP called “X”historically would have 10,000 impressions available. If thepolarization percentage for MP “X” is 60% for a VC describing maleviewership and the campaign in question is targeting male viewers, thenduring the runtime for the campaign it follows that there will be 6,000on-target viewers receiving impressions. The campaign will however haveto pay for the entire 10,000 impressions in order to provide the 6,000on target impressions. After this MP having the lowest cost per pointhas been added to the campaign, the system evaluates S506 whether or notthe aggregated on-target inventory fills the campaign GRP requirements.If not, the flow proceeds S508 to locate the next MP having the nextlowest cost per point where the polarization is in line with campaigntargeting. Subsequently step S504 is executed again for this next MP.

Upon adding an MP to the campaign, should it be determined per step S506that the campaign GRP requirements would be fulfilled, the flow proceedsto S510 where a quotation for the campaign in terms of price and GRPs isprepared and provided to the client/advertiser. Optionally theclient/advertiser will be advised on how the quoted price compares withany established budget.

According to flow chart 600 of FIG. 6, a Client/Advertiser supplies alist of MPs to be targeted and may specifically add MPs to a campaign.The system according to the invention automatically determines 502 the“cost per point” or cost per polarization probability percentage foreach MP, and advises the client. Thus when selecting MPs to add to thecampaign, the Client can be aware of the cost-effectiveness of each suchaddition.

Per step S604, a client chooses a targeted MP to add to the campaign.For the targeted MP, the system according to the invention thendetermines available inventory and the inventory portion that matchescampaign targeting (the “Targeted Inventory” that is determined based onthe MPs polarization probability). This targeted inventory portion isthen aggregated into the available targeted inventory.

After this MP has been added to the campaign, the system evaluates S606whether or not the aggregated on-target inventory fulfills the campaignGRP requirements. If not, the flow proceeds S608 where the client isadvised on total predicted reach so far, and the client proceeds tochoose the next MP to add to the campaign. The flow then reverts to stepS604 where the client chooses another MP to add to the campaign.

Upon adding an MP to the campaign, should the system according to theinvention determine per step S606 that the campaign GRP requirementswould be fulfilled, the flow proceeds to S610 where a quotation for thecampaign in terms of price and GRPs is prepared and provided to theclient/advertiser. Optionally the client/advertiser will be also advisedon how the quoted price compares with any established budget.

A simplified example might include a campaign that has two target sites.Site #1 has a polarization factor of 60% male and site #2 has apolarization factor of 70% male. The campaign is targeting males. So,for site #1 the campaign would need to purchase 100 impressions to get60 on target, and for site #2 the campaign would we need to purchase 100impressions to get 70 on target. If the campaign goal was to reach 130on-target impressions, 60 would be from site #1 and 70 from site #2. Ifthe campaign goal was higher, but the available inventory on sites #1and #2 was only 100 impressions each, the demand side platform wouldtell the client that only 130 on-target impressions can be deliveredunless they add more sites to the campaign. Also for this simplifiedexample, according to FIG. 5 site #2 would be added to the campaignbefore site #1.

The overall flow for operation of an electronic advertising campaignaccording to an exemplary embodiment of the invention is shown in FIG.7, including validation of GRPs delivered and adjustment of anefficiency factor. As mentioned earlier, information is typicallyavailable from third-party organizations such as Nielsen after anadvertising campaign has been run that quantify the reach or GRPsachieved by the campaign. Since the method for estimating and providinga GRP quote before a campaign has run involves statisticalpredictability, the ability to reconcile with actual achieved reachafter a campaign is implemented through the “efficiency factor” is animportant component of the process. As the efficiency factor is adjustedfrom time to time comparing predicted results with real results, theability to predict future campaigns with greater accuracy is improved.By performing this reconciliation from time to time and not after eachcampaign has run, the efficiency factor is adjusted in a controlledenvironment rather than on random campaigns. This avoids the possibilitythat individual campaigns may have unique or even aberrant differencesthat would throw off the result to some extent.

Per flow chart 700 of FIG. 7, impressions required to be purchased toachieve an advertiser/client's desired GRP amount are estimated S702given the client's targeting criteria and the client's desired campaignrun time. In step S704, a price quote for requested GRPs is provided tothe advertiser/client. Per step S706, the advertising campaign is rununtil the required GRPs are achieved or until the budget is spent. Then,in step 708, data is acquired from Nielsen or a similar source includingactual GRPs delivered for the campaign. In step 710, from time-to-timeand based on results from one or more campaigns, the difference betweenGRPs estimated prior to campaigns and the Nielsen data for GRPs actuallydelivered is calculated. A comparison can also be made between theNielsen GRP data and GRPs recorded by the demand side platform for eachcampaign. Finally per step S712, the Efficiency Factor is adjusted asrequired to compensate for the differences between estimated/quoted GRPsand Nielsen GRP data, or alternately between GRPs delivered as recordedby the demand side platform and the Nielsen GRP data.

Quoting GRPs Based on MP Polarization and Viewer Polarization

When campaign reach is estimated based only on site polarizationprofiles, determining the available on-target inventory for a campaignis performed with respect to FIGS. 4-6. For these estimates, the processis limited to the polarization of the sites. If a site has a 60% malepolarization and a campaign is targeting males, then on average it isexpected that up to 40% of the impressions purchased on this site willbe essentially wasted. In order to get the on-target rate above that ofthe sites themselves, it is necessary to also take into account thepolarization of the viewers in the known viewer database, and pass overunknown viewers to focus on known polarized (or look-alike polarized)viewers.

At the same time, if the campaign passes over unknown viewers in favorof known polarized viewers, the effective available inventory could godown significantly, and with it the statistical confidence that therequirements for a campaign being estimated and quoted can be fulfilled.One solution is to mix in some unknown viewers, and the strategy formixing can be validated by comparing GRP results with pre-campaignestimations after campaigns are run in a similar way that the EfficiencyFactor is utilized per FIG. 7.

Assuming the database of known viewers—including known polarized andlook-alike polarized viewers—is large enough to encompass a majority ofpotential viewers of a campaign, and assuming that a campaign istargeting MPs that have a reasonable degree (above 70%) of polarizationfor targeted VCs, mixing in unknown viewers may not dilute the campaignsignificantly since it would be expected that polarized viewers would bevisiting the polarized MP anyway.

To get substantial benefit from known viewers, a campaign would have topass over unknown viewers at least in the early part of the operationalperiod or run time of a campaign. As a campaign progresses, the viewersreceiving ad impressions for the campaign on targeted sites can betracked by a system according to the invention. As long as the successrate for on-target impressions is such that the campaign is on track tobe fulfilled during the allotted run time, no additional unknown viewerswould need to be mixed in. If the success rate drops below this, thenunknown viewers would be added in including a larger percentage ofwasted impressions.

Alternately, a campaign can start by targeting a mix of known andunknown viewers and then adjust the mix during the run-time based on thesuccess rate for on-target impressions. How the initial mix isdetermined can depend on the polarization probabilities for targetedsites and the quantity of known polarized and look-alike polarizedviewers in the known viewer database that match the targeting criteria.Again, the correlation between GRPs estimated prior to a campaign andGRPs delivered during a campaign is validated after a campaign bycomparing with GRP data such as that supplied by Nielsen. If on averagethere are discrepancies, then the method for determining the initial mixof known and unknown viewers can be appropriately adjusted.

The foregoing detailed description has set forth a few of the many formsthat the invention can take. It is intended that the foregoing detaileddescription be understood as an illustration of selected forms that theinvention can take and not as a limitation to the definition of theinvention. It is only the claims, including all equivalents that areintended to define the scope of this invention.

At least certain principles of the invention can be implemented ashardware, firmware, software or any combination thereof. Moreover, thesoftware is preferably implemented as an application program tangiblyembodied on a program storage unit, a non-transitory user machinereadable medium, or a non-transitory machine-readable storage mediumthat can be in a form of a digital circuit, an analog circuit, amagnetic medium, or combination thereof. The application program may beuploaded to, and executed by, a machine comprising any suitablearchitecture. Preferably, the machine is implemented on a user machineplatform having hardware such as one or more central processing units(“CPUs”), a memory, and input/output interfaces. The user machineplatform may also include an operating system and microinstruction code.The various processes and functions described herein may be either partof the microinstruction code or part of the application program, or anycombination thereof, which may be executed by a CPU, whether or not suchuser machine or processor is explicitly shown. In addition, variousother peripheral units may be connected to the user machine platformsuch as an additional data storage unit and a printing unit.

What is claimed is:
 1. In a digital medium environment of real-timebidding and selection of advertising opportunities corresponding toviewers simultaneously accessing websites via computing devices, acomputerized method for efficiently providing bid responses andadvertising over an online network, comprising: establishing, by one ormore processors, a database of polarized media properties andpolarization probabilities for the polarized media properties, whereinthe polarization probabilities comprise, for a first polarized mediaproperty of the polarized media properties, a first polarizationprobability corresponding to a first viewer characteristic and a secondpolarization probability corresponding to a second viewercharacteristic; receiving, by the one or more processors, desired grossrating points for an online ad campaign; receiving targeting parametersfor the online ad campaign, wherein the targeting parameters comprisetargeted geographic locations, targeted polarized media properties, andtargeted viewer characteristics, and wherein the targeted polarizedmedia properties comprise the first polarized media property and thetargeted viewer characteristics comprise the first viewer characteristicand the second viewer characteristic; determining, by the one or moreprocessors, a number of impressions to achieve the desired gross ratingpoints for the online ad campaign based on the targeted polarized mediaproperties, the desired gross rating points, the targeted viewercharacteristics, the targeted geographic locations, and the polarizationprobabilities for the targeted polarized media properties in thedatabase of polarized media properties including, for the firstpolarized media property, the first polarization probabilitycorresponding to the first viewer characteristic and the secondpolarization probability corresponding to the second viewercharacteristic; running the online ad campaign based on the number ofimpressions to achieve the desired gross rating points by: providing anadvertisement for display to a first viewer, wherein the advertisementis selected using the targeting parameters; receiving an impressionopportunity from a remote server, wherein the impression opportunityreferences a second viewer and a media property; and responding to theimpression opportunity, by the one or more processors, by sending a bidresponse to the remote server based on a determination that (i) thetargeting parameters match characteristics of the second viewer and themedia property and (ii) the desired gross rating points have not beenachieved.
 2. The computerized method of claim 1, further comprising:determining a historical cost of purchasing impressions on each targetedpolarized media property of the targeted polarized media properties; andpredicting, by the one or more processors, a cost for the number ofimpressions to achieve the desired gross rating points for the online adcampaign, based at least in part on the historical cost of purchasingimpressions on each targeted polarized media property, and thepolarization probability of each targeted polarized media property withrespect to each targeted viewer characteristic.
 3. The computerizedmethod of claim 2, further comprising: determining, by the one or moreprocessors, a price quote for the online ad campaign based on at leastthe predicted cost of the number of impressions to achieve the desiredgross rating points for the online ad campaign and a profit marginfactor.
 4. The computerized method of claim 3, wherein determining theprice quote comprises determining a number of impressions to purchasefor the first targeted polarized media property based on desiredon-target impressions, the first polarization probability for the firstpolarized media property with respect to the first viewer characteristicand the second polarization probability for the first polarized mediaproperty with respect to the second viewer characteristic.
 5. Thecomputerized method of claim 3, further comprising: determining theprice quote based on an efficiency factor, wherein the efficiency factoris determined based on a difference between historical gross ratingpoints desired and historical gross rating points delivered.
 6. Thecomputerized method of claim 5, further comprising: determining, by theat least one processor and after running the online ad campaign, anactual gross rating points delivered via the online ad campaign; andmodifying the efficiency factor by comparing the desired gross ratingpoints with the actual gross rating points delivered via the online adcampaign.
 7. The computerized method of claim 1, further comprisingdetermining that the desired gross rating points have not been achievedby determining that the number of impressions have not been purchased.8. The computerized method of claim 1, further comprising determining acost of on-target impressions for one or more targeted polarized mediaproperties based on the polarization probabilities for the one or moretargeted polarized media properties and a cost of impressions for theone or more targeted polarized media properties; and selecting targetedpolarized media properties to include in the online ad campaign based onthe determined cost of on-target impressions for the one or moretargeted polarized media properties.
 9. The computerized method of claim1, wherein determining the number of impressions to achieve the desiredgross rating points further comprises generating a compositepolarization probability for the first polarized media property based onthe first polarization probability and the second polarizationprobability and determining the number of impressions to achieve thedesired gross rating points based on the composite polarizationprobability for the first polarized media property.
 10. The computerizedmethod of claim 1, further comprising: determining a cost per point foreach targeted polarized media property of the targeted polarized mediaproperties; and selecting a set of targeted polarized media propertiesto include in the online ad campaign based on the determined cost perpoint for each targeted polarized media property.
 11. The computerizedmethod of claim 10 further comprising: determining an available targetedinventory for the selected set of targeted polarized media properties;determining, by aggregating the available targeted inventory, that theavailable targeted inventory will achieve the desired gross ratingpoints; and in response to determining that the available targetedinventory will achieve the desired gross rating points, generating agross rating points quote based on the selected set of targetedpolarized media properties.
 12. The computerized method of claim 10further comprising: determining an available targeted inventory for theselected set of targeted polarized media properties; determining, byaggregating the available targeted inventory, that the aggregatedavailable targeted inventory will not achieve the desired gross ratingpoints; in response to determining that the aggregated availabletargeted inventory will not achieve the desired gross rating points,selecting at least one more polarized media property to add to theselected set of targeted polarized media properties; and determining, bythe one or more processors, a revised aggregated available targetedinventory based on the selected set of targeted polarized mediaproperties, including the at least one more polarized media property;and in response to determining that the revised aggregated availabletargeted inventory achieves the desired gross rating points, generatinga gross rating points quote.
 13. A system for efficiently providing bidresponses and advertising over an online network in a digital mediumenvironment of real-time bidding and selection of advertisingopportunities corresponding to viewers simultaneously accessing websitesvia computing devices, the system comprising: at least one processor;and at least one non-transitory computer readable storage medium storinginstructions that, when executed by the at least one processor, causethe system to: establish a database of polarized media properties andpolarization probabilities for the polarized media properties, whereinthe database comprises viewer characteristics corresponding to thepolarized media properties, wherein the polarization probabilitiescomprise, for a first polarized media property of the polarized mediaproperties, a first polarization probability corresponding to a firstviewer characteristic and a second polarization probabilitycorresponding to a second viewer characteristic; receive desired grossrating points for an online ad campaign; receive targeting parametersfor the online ad campaign, wherein the targeting parameters comprisetargeted geographic locations, targeted polarized media properties, andtargeted viewer characteristics, and wherein the targeted polarizedmedia properties comprise the first polarized media property and thetargeted viewer characteristics comprise the first viewer characteristicand the second viewer characteristic; determine a number of impressionsto purchase to achieve the desired gross rating points for the online adcampaign based on the targeted polarized media properties, the desiredgross rating points, the targeted viewer characteristics, the targetedgeographic locations, and the polarization probabilities for thetargeted polarized media properties in the database of polarized mediaproperties including, for the first polarized media property, the firstpolarization probability corresponding to the first viewercharacteristic and the second polarization probability corresponding tothe second viewer characteristic; execute the online ad campaign basedon the number of impressions to achieve the desired gross rating pointsby: providing an advertisement for display to a first viewer, whereinthe advertisement is selected using the targeting parameters; receivingan impression opportunity from a remote server, wherein the impressionopportunity references a second viewer and a media property; andresponding to the impression opportunity by sending a bid response tothe remote server based on a determination that (i) the targetingparameters match characteristics of the second viewer and the mediaproperty and (ii) the desired gross rating points have not beenachieved.
 14. The system of claim 13, further comprising instructionsthat, when executed by the at least one processor, cause the system to:determine a historical cost of reaching viewers on each targetedpolarized media property of the targeted polarized media properties; andpredict a cost for the number of impressions to achieve the desiredgross rating points for the online ad campaign, based at least in parton the historical cost of reaching viewers on each targeted polarizedmedia property and the polarization probability of each targetedpolarized media property with respect to each targeted viewercharacteristic.
 15. The system of claim 14, further comprisinginstructions that, when executed by the at least one processor, causethe system to: determine a price quote for the online ad campaign basedon at least the predicted cost of the number of impressions to purchaseto achieve the desired gross rating points for the online ad campaignand a profit margin factor.
 16. The system of claim 15, furthercomprising instructions that, when executed by the at least oneprocessor, cause the system to determine the price quote by determininga number of impressions to purchase for a targeted polarized mediaproperty based on desired on-target impressions and a polarizationprobability for the targeted polarized media property with respect to atargeted viewer characteristic.
 17. The system of claim 15, furthercomprising instructions that, when executed by the at least oneprocessor, cause the system to determine the price quote based on anefficiency factor, wherein the efficiency factor is determined based ona difference between historical gross rating points desired andhistorical gross rating points delivered.
 18. The system of claim 17,further comprising instructions that, when executed by the at least oneprocessor, cause the system to: determine, after running the online adcampaign, the actual gross rating points delivered via the online adcampaign; and modify the efficiency factor by comparing the desiredgross rating points with an actual gross rating points delivered via theonline ad campaign.
 19. The system of claim 13, further comprisinginstructions that, when executed by the at least one processor, causethe system to: determine a cost of on-target impressions for one or moretargeted polarized media properties based on the polarizationprobabilities for the one or more targeted polarized media propertiesand a cost of impressions for the one or more targeted polarized mediaproperties; and select targeted polarized media properties to include inthe online ad campaign based on the determined cost of on-targetimpressions for the one or more targeted polarized media properties. 20.The system of claim 13, further comprising instructions that, whenexecuted by the at least one processor, cause the system to determinethe number of impressions to purchase to achieve the desired grossrating points by generating a composite polarization probability for thefirst polarized media property based on the first polarizationprobability and the second polarization probability and determining thenumber of impressions to achieve the desired gross rating points basedon the composite polarization probability for the first polarized mediaproperty.
 21. The system of claim 13, further comprising instructionsthat, when executed by the at least one processor, cause the system to:determine a cost per polarization probability percentage for eachpolarized media property in the database of polarized media propertiesbased on the polarization probabilities for each polarized mediaproperty; and select a set of targeted polarized media properties toinclude in the online ad campaign based on the determined cost perpolarization probability percentage for each polarized media property.22. The system of claim 21, further comprising instructions that, whenexecuted by the at least one processor, cause the system to: determinean available targeted inventory for the selected set of targetedpolarized media properties; determine, by aggregating the availabletargeted inventory, that the available targeted inventory will achievethe desired gross rating points; and in response to determining that theavailable targeted inventory will achieve the desired gross ratingpoints, generating a gross rating points quote based on the selected setof targeted polarized media properties.
 23. The system of claim 21,further comprising instructions that, when executed by the at least oneprocessor, cause the system to: determine an available targetedinventory for the selected set of targeted polarized media properties;determine, by aggregating the available targeted inventory, that theaggregated available targeted inventory will not achieve the desiredgross rating points; in response to determining that the aggregatedavailable targeted inventory will not achieve the desired gross ratingpoints, select at least one more polarized media property to add to theselected set of targeted polarized media properties; determine a revisedaggregated available targeted inventory based on the selected set oftargeted polarized media properties, including the at least one morepolarized media property; determine that the revised aggregatedavailable targeted inventory achieves the desired gross rating points;and in response to determining that the revised aggregated availabletargeted inventory achieves the desired gross rating points, generatinga gross rating points quote.